Minimizing maintenance anomalies in expert systems

Abstract Expert systems are emerging as a powerful technology for solving many problems previously requiring human experts. However, maintenance has been identified as a major difficulty in expert system implementations. Surprisingly, the problem of maintenance has only recently begun to receive attention in expert systems research, though it has long been an issue in databases. Databases are in a constant state of change, and the prevention of maintenance anomalies is essential. As similar maintenance operations are performed on rule bases, this paper investigates techniques to avoid maintenance anomalies in expert system rule bases. The result is an expert system rule base structure that is appropriate for volatile production use. In addition to lower maintenance demands, this approach favorably impacts on verification, computational efficiency, and storage requirements.

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